System and method of a two-step object data processing by a vehicle and a server database for generating, updating and delivering a precision road property database

ABSTRACT

A method and a system of a two-step object data processing by a vehicle and a database for generating and updating a digital road description database containing object-based information about road objects is disclosed. First, the server database comprises fourth data sets and generates and forwards first data sets to the vehicle, which are related to the area of interest of the vehicle. The vehicle is collecting a plurality of ambient data sets at least along a specific section of its path. It is performing the first step of object data processing by evaluating a selection of the plurality of ambient data sets and generating at least one second data set comprising at least location information and detailed object-based information. It further generates third data sets containing differences between the object-based information of the second data sets and the object-based information of the first data sets and forwards the third data sets to the server database. The server database performs the second step of object data processing comprising at least statistical evaluation and post processing to update the fourth data sets in the server database based on the third data sets.

FIELD OF THE INVENTION

The invention relates to a method and a system for collecting road dataand environmental data of the vicinity of a road. There is a two-stepobject data generation and processing shared between a vehicle and aserver database for generating data sets comprising driving-relevantobject data. It further relates to a database containing such data, amethod for building such a database and a method for distributing suchdata to vehicles.

DESCRIPTION OF THE RELATED ART

For enhancing the operation of vehicles and for providing new featuresand functions an increasing number of sensors and processors orcomputers are integrated in modern vehicles. In U.S. Pat. No. 6,353,785B1, a method and system for in-vehicle computer architecture isdisclosed. The computing architecture includes a data network comprisedof a plurality of interconnected processors, a first group of sensorsresponsive to environmental conditions around the vehicle, a secondgroup of sensors responsive to the vehicle's hardware systems, and a mapdatabase containing data that represent geographic features in thegeographic area around the vehicle. The vehicle operations programmingapplications may include adaptive cruise control, automated mayday, andobstacle and collision warning systems, among others. In thisembodiment, the vehicle needs a continuous communication over a datanetwork which may sometimes be interrupted under real road conditions.Furthermore, a precise map database is required.

EP 1 219 928 A1 shows a method for generating road segments for adigital map. A vehicle equipped with a camera is driving over a roadsection. The captured images are correlated with GPS information toproduce a digital map.

To generate road maps, highly specialized vehicles are used. They haveexpensive equipment for scanning roads which may cost significantly morethan the vehicle itself. After scanning the roads, manual processing ofthe acquired information is required. This is expensive, labourintensive and prone to errors. Therefore, only comparatively long updatecycles can be achieved.

SUMMARY OF THE INVENTION

The problem to be solved by the invention is to improve the knownvehicles, databases and methods for building databases which furtherprovide sufficient information to vehicles to enable Highly AutomatedDriving (HAD), or autonomous driving, or self-driving, or other vehiclecontrol applications, or precision vehicle positioning.

Solutions of the problem are described in the independent claims. Thedependent claims relate to further improvements of the invention.

Tests and simulations with HAD and self-driving vehicles have shown thata very detailed knowledge of the vehicle's environment and specificallyof the road is required. Such data may be used for a plurality of otherapplications like high-beam assist, speed advisor, bump/pothole warning,economic driving, smart cruise control, advanced shifting, acceleratorforce feedback pedal, smart energy management, damping control, advancedretarder control, cooling strategy, emergency steering assist, previewESC, intelligent parking, traffic light assist and curve speed warning.Therefore, it is desirable to provide fresh and up-to date data of theroad and other driving-relevant objects at moderate or low cost.

Such data is preferably based on data acquired by sensors in thevehicle. But it would be desirable to have road information beyond thereach of the sensors, for example in a long distance or behind a curve.Such information may allow a proactive control of the vehicle. If forexample, the vehicle is approaching an intersection behind a curve, thespeed of the vehicle may be reduced, or a different gear may be selectedprior to an inclining road.

Furthermore, such sensors may be impaired by internal errors or byenvironmental conditions. For example, a camera tracking the road, maybe impaired by fog or heavy rain. In such a case, it would be desirableto have detailed information about the road which allows to correlatethe captured camera image with the known information. This way it may bepossible to interpret information in a correct way, which otherwisewould be hard to recognize. If, for example, the vehicle knows preciselythe positions of each dash of a dashed centreline and its exact distanceto the border of the road, a positioning may still be possible, even ifthe camera image is extremely noisy and the centreline dashes are hardto detect.

If sufficiently detailed, fresh and reliable information about the roadand other driving-relevant objects is available, the field of use of theabove mentioned vehicle applications may be broadened or therequirements on the vehicles' sensors may be lowered and vehicles may beequipped with less or cheaper sensors. Furthermore, driving may bepossible with failed or impaired sensors.

Conventional digital maps of the road, which are used today inconjunction with GNSS tracking of vehicle movements may be sufficientfor navigation, but they are not detailed enough for self-drivingvehicles. Scanning the roads with specialized scanning vehicles providesmuch more details, but is extremely complex, time-consuming andexpensive. Therefore, there are large update intervals which may reachan order of years. From a global view, the road infrastructure itselfdoes not change quickly, as road construction is expensive andtime-consuming. A road between any two points will remain for a longperiod, but road details may change quickly. Therefore, it is desired toprovide sufficiently detailed and precise road information or roadproperty data in a timely manner.

The basic concept of a preferred embodiment of generating and updating aprecision road property database is a two-step object data processing,whereby a first object data processing step is done by a vehicle and asecond object data processing step is done by a server database.

In a first embodiment, a standard production vehicle uses its sensors tocollect a plurality of ambient data sets, processes them andcommunicates with a precision road property database on a server toimprove the quality of its collected data and to provide the serverdatabase updated ambient information. Generally, the information ismostly object-based and related to driving-relevant objects like a roadobject, a road furniture object, a geographic object or a furtherobject. A road object preferably is related to a road itself. It maydescribe basic characteristics of a road like the width, the direction,the curvature, the number of lanes in each direction, the width oflanes, the surface structure. It may further describe specific detailslike a curb stone, a centreline, even a single dash of a dashedcentreline or other markings like a crosswalk or a stop line. A roadfurniture object is related to road furniture, also called streetfurniture. Road furniture may comprise objects and pieces of equipmentinstalled on streets and roads for various purposes. It may includebenches, traffic barriers, bollards, post boxes, phone boxes,streetlamps, traffic lights, traffic signs, bus stops, tram stops, taxistands, public sculptures, and waste receptacles. A geographic objectmay be any stationary object like but not limited to a building, ariver, a lake or a mountain. Further objects may be objects which arenot falling into one of the above categories, but are also relevant fordriving. Such objects may be trees, stones, walls or other obstaclesclose to a road. They may also comprise structures like trenches, planesurfaces and others which may be considered for planning alternate,emergency exit or collision avoidance paths.

The vehicle may be a car, a truck, a motorbike or any other means fortraveling on a road along a path. The embodiments are described hereinby example of a vehicle, but they are generally applicable to aplurality of vehicles. A vehicle preferably has a number of sensorslike, but not limited to a camera system like a CCD camera which may besuitable for capturing visible and/or infrared images. Preferably asimple mono-camera is provided. Alternatively, a stereo camera, whichmay have two imaging sensors mounted distant from each other may beused. There may be further sensors like radar sensors, laser sensors,infrared sensors, ultrasound transducers or other environmental sensors.Although it is preferred that the sensors are oriented to the drivingdirection, they may be oriented to other directions like sideward or tothe rear of the vehicle. It is further preferred that the vehicle has atleast one computer or a plurality of computers, together with memory andsoftware for performing the methods described herein. Most preferably acomputer has enough processing power and memory for processing thesensor signals. Furthermore, the vehicle preferably has communicationmeans for communicating over a communication network which may be, forexample but is not limited to, a mobile communications network, Wi-Fi,satellite radio or any other type of network, which may even be a cablenetwork. In general, communication between the vehicle and the serverdatabase is established by such communication means. The vehicle may usedifferent communication means dependent on availability or cost, like amobile communications network on route and Wi-Fi close to hotspots or athome.

The precision road property database further referred to as serverdatabase if not otherwise mentioned, preferably is provided by a serviceprovider and preferably is hosted on a plurality of host computersystems. It may be a central database, but preferably it is split into aplurality of sub databases, which for example are divided by geographicregions. Preferably, a database manager is connected to the serverdatabase. Most preferably, the database manager comprises a plurality ofsub-managers connected to sub databases. Preferably, the server databasehas a suitable network connection like an Internet connection tocommunicate with the vehicle. In an alternate embodiment, thecommunication with the vehicles may be performed via third partycommunication systems like back ends of vehicle manufacturers. Thebandwidth should be large enough to communicate with a large number ofvehicles simultaneously. Preferably, the communication is compressedand/or encrypted. Preferably, the server database comprises a physicaldata storage which may be in specific server locations, in a cloud basedshared infrastructure, a combination of approaches or similarembodiment, together with a software for managing data storage and dataflow. Herein, the term server database is used meaning all relatedsystems in a broad sense. This may include a computer system, a physicalstorage or a cloud based shared infrastructure, a database engine, adatabase manager and further software for processing data in thedatabase or similar embodiment.

Preferably, the vehicle gets road property data before drivingautonomously and stores it in a vehicle database. An initial data setmay be supplied by a vehicle manufacturer during a manufacturing orinitial initialization procedure of the vehicle, by a data storagemedium like a CD, DVD, hard disk, portable memory, or by download fromthe server database. As the initial data set may be comparatively large,it is preferred to have a high-speed data connection available for aninitial download from the server database. Preferably, the initial dataset covers an area or region in which the vehicle may operate in thenear future. After the initial data set has been received, the vehiclemay require further updates. Such updates may provide new informationabout changed objects and/or an extension or change of the previouslycovered area. The vehicle may request such updates from the serverdatabase in certain intervals and/or if a high speed network connectionis available and/or if an extension of coverage is required, which maybe the case after a new route has been planned or even during driving.The server database may also trigger a data transfer to the vehiclebased on its knowledge, which data may be required by the vehicle. Suchdata preferably is updated and/or urgent data which may include warningsof dangerous road conditions.

For providing initial or ongoing data sets to a vehicle, the serverdatabase generates at least one first data set from fourth data setsstored in the server database. The at least one first data set isrelated to the request by the vehicle or the trigger by the serverdatabase. Preferably, the first data set covers an area of interest forthe vehicle. Such an area of interest may be modified by the serverdatabase or the vehicle over the time of the vehicle's use. Preferably,the path driven by the vehicle is part of its area of interest.Preferably, the server database has stored information about the statusof the vehicle database. Such information may comprise a copy of thefirst data sets stored in the vehicle or at least one identifier foridentifying the first data sets stored in the vehicle. This statusinformation is preferably updated after the vehicle has confirmedreception of first data sets. By using this status information, thefirst data set may only comprise amended data sets and/or new data sets.There may be data sets which the vehicle does not have, data sets, whichthe vehicle has, but which are updated, data sets, which the vehiclehas, but which are retransmitted, or data sets which the server databasewants the vehicle to delete. If triggered by the server database, the atleast one first data set may contain updated data and/or warning dataand/or data which should be verified by the vehicle. Preferably, eachfirst data set comprises at least one location information and detailedobject-based information. The location information specifies a position,an area or region to which the detailed object based information isapplicable. This includes, that a data set may comprise one locationinformation and a plurality of object-based information which preferablyrelate to the same or adjacent locations. The object based informationmay comprise detailed information about at least one driving-relevantobject like a road object, a road furniture object and a geographicobject.

Preferably, the data sets provided by the server database to vehiclesand from vehicles to the server database comprise an object-based 3Dvector format. Most preferably, the first, second, third and fourth datasets comprise such a vector format. Such a vector format may be a vectorgraphics format, preferably like a standard vector graphics format asused in computer game or CAD drawing technologies or a derivativethereof. Preferably, detailed objects of a road like limits of the road,markings of the road, inter-sections and exits as well as streetfurniture are represented in a 3D vector graphics format. Due to avector graphics format, only low volumes of data have to be transferredbetween a vehicle and a server database and back. Also, a vectorgraphics format allows to render the road property data in differentways, to represent different viewing angles or sensor styles such ascamera-like or radar-like.

Preferably, the server database supplies auxiliary information to thevehicle as part of the first data sets. Such auxiliary information maybe a server database generated confidence level or other confidencerelated information, herein called confidence level, and importancelevel, and urgency level or statistical information like average valuesand standard deviations. This auxiliary information may be supplied foreach individual data set or for a group of data sets.

After the first data sets have been generated, they are forwarded to thevehicle and stored in the vehicle database. From the vehicle database,the vehicle can derive driving-relevant data for its driving controlapplications like highly automated driving.

But vehicles are not only consumers of road property data. They alsogenerate road property data by analysing ambient data sets collected bytheir sensors. Under normal road and driving conditions, it may bepreferred, if vehicles sample or collect information by their sensors,preferably also by their image sensors while driving, whileparticipating in traffic or at least while being on the road or inproximity to a road and compare and/or correlate the sampled informationwith the previously downloaded or stored information on a regular orperiodic base. The frequency or time intervals between such collections,which may also be called sampling rate, may be determined by the sensorsin the vehicle, their resolution and/or the processing power in thevehicle. Instead of periodic sampling, random sampling may be chosen.The frequency of sampling may also be adapted to the environment. In thecase of a curvy road, a higher sampling rate may be desired compared toa straight road. Different sapling rates may also be requested by theserver database.

The sampled ambient data sets may be recorded and stored over a certaintime as a sequence of ambient data sets. This allows to process thestored ambient data sets as a background task and to move back and forththrough the stored data sets for more sophisticated processing incontrast to real-time processing which has to follow the vehicle atspeed. Storage of a sequence of sampled ambient data sets allows thevehicle specifically when driving, to monitor for the objects appearanceover time and/or distance. For example, a traffic sign may be a smallspot on an image taken by a camera from a large distance. Whenapproaching the traffic sign, this spot increases and starts to show acharacteristic pattern until it can be seen clearly in a close up viewshortly before passing the traffic sign.

A continuous recording of ambient data sets at least over a certain timeinterval may also allow a view-back. In the above example, the vehiclemay have identified the traffic sign just before passing the trafficsign. It is now possible to roll back the ambient data sets, such asimages, when approaching the traffic sign and to generate furtherinformation how such a traffic sign looks like from a distant approachor where it is exactly located. This data may also be used as part ofthe driving-relevant data to enable vehicles to detect the traffic signfrom a larger distance.

The server database may transmit a sampling request message, whichpreferably specifies the time and/or location of which informationshould be sampled. Furthermore, the server database may request aspecial processing of a specific object or area. For example, it mayrequest text or language processing of a traffic sign. It may furtherrequest a high resolution image of an unknown area, for example atraffic sign, such that the server database itself may do additionalprocessing such as text recognition in a foreign language. Specialrequests may preferably be addressed to specific vehicles which may havespecific equipment for example like a stereo camera or a laser scanneror which move in a certain region. The server database may also uploadspecific processing instructions or specific program code. Such specialprocessing requests may be contained within at least one first data set,either as implicit instructions or by means of parameters or data. Forexample, a low confidence level would trigger a rescan of an object toget better information and therefore to increase the confidence level.

Sampling may also be triggered based on confidence and/or importanceand/or urgency levels. If a first data set in the vehicle database showsa comparatively low confidence level or a high importance and/or urgencylevel at a certain location, it would be preferred, if a large number oralmost all vehicles passing or staying at that location would collectinformation by their sensors and forward updates to the server database.Over time, an increasing number of data is collected which leads to ahigher confidence level. As it may not be necessary to rescan objectswith a higher confidence level, the number of rescanned objectsdecreases with time. Accordingly, the network traffic and the requiredprocessing power in the vehicle and the server database may decrease.

Furthermore, there may be a priority scheme to better utilizeless-expensive communication networks if a high number of vehicles aretransmitting updates at the same time. If for example, the confidencelevel of a data set is comparatively low and the sensors deliverimproved data, the vehicle may record such data. If the confidence levelis very high, only a low number of differences may be recorded. In sucha case, the server database may further increase the confidence level.If the data already has a maximum confidence level, the vehicle will notrecord further data in that area. Instead it may generate at least oneconfirmation data set or send a confirmation message to the serverdatabase about passing a certain road section without having identifiedany differences. This way, the communication volume and thuscommunication cost may decrease with growing confidence in the data.

The processing power in some vehicles may not be sufficient to do areal-time comparison of data provided by the vehicle database and thedata acquired by the vehicle's sensors for the complete vehicle pathdriven, at least with high frame rates, which may be adequate for highspeed driving vehicles. A vehicle, which is driving at 120 km/h proceedswith 33.3 m/s. To get a spatial resolution of a sequence of ambient datasets of about 1 m, 33.3 ambient data sets must be processed per second.Comparing the vehicle database information with the ambient data sets inparallel to other driving-related tasks, may overburden most vehicle'scomputers, in particular as a single ambient data set may comprise alarge number of information of a plurality of sensors like cameras,radar sensors, infrared or RF sensors and more, like other environmentalsensors. Therefore, the comparison is preferably done in larger,discontinuous sections of the vehicle's path, as can be handled by eachvehicle. The length of the vehicle path sections may be in a range froma few metres to some hundred metres and follow each other with a gap ofa few metres to some hundred metres, which may further depend on avehicle's speed or the currently available processing power. In afurther embodiment, the evaluation of different information may be madeindependently. For example, visible camera images may be evaluated inreal time, while laser scanned images may be evaluated at a later time.In a further embodiment, a certain level of evaluation of informationmay be done close to real-time, following the vehicle at speed, while amore detailed evaluation of information may only be done if differencesare detected.

All or at least a selection as mentioned above of the road ambient datasets collected by the sensors are further evaluated to generate at leastone second data set by the vehicle, each second data set comprising atleast location information and detailed object-based information aboutat least one of a road object, a road furniture object and a geographicobject. To generate such second data sets, the stored ambient data setsare evaluated. Preferably, the vehicle identifies driving-relevantobjects either by the stored ambient data sets alone or by correlationof the stored ambient data sets with the information from at least onefirst data set as stored in the vehicle database. Such a correlation maybe useful, if an object is hard to identify, which may for example becaused by a poor viewing angle, poor weather conditions or if the objectis at least partially hidden. Preferably, the vehicle uses advancedtechniques for increasing the object description accuracy likestatistical evaluation or grouping of objects which will be describedlater in more detail. The second data sets represent the current view ofthe driving-relevant objects by the vehicle. Generating at least onesecond data set and/or generating at least one third data set is thefirst object data processing step which is done by the vehicle.

After generating the second data sets, the vehicle generates third datasets containing differences between the object-based information of thesecond data sets and the object-based information of the first data setsas stored in the vehicle database. Additionally or instead, at least onethird data set may comprise a second data set without a calculateddifference. Furthermore, third data sets may contain partial ambientdata sets, complete ambient data sets, sequences of ambient data setsand other auxiliary information from sensors and the vehicle status aswell as intermediate processing results. Preferably, the area ofcoverage of the third data sets is limited as requested by the serverdatabase or as determined by the vehicle. This selection has beendescribed above in detail. It is further preferred to generate only suchthird data sets, when there are at least a minimum of differencesbetween the object-based information of the second data sets and theobject-based information of the first data sets, except such data setshave been expressly requested by the server database. As an alternativeto not generating certain data sets, the data sets may still begenerated but marked that no transmission to the server database shouldbe made. If the vehicle has identified important data and generated atleast one second data set without a corresponding first data set, it maybe assumed, that the vehicle has identified a new object. Preferably,third data sets are always generated for such new objects. The thirddata sets may contain auxiliary data which may give further informationlike a vehicle generated confidence level. Such a vehicle generatedconfidence level may be determined by the vehicle based on the equipmentof the vehicle, traveling speed, number of measurements or images or ona signal to noise ratio or any other measurement parameter. There may befurther parameters to influence the vehicle generated confidence levellike weather conditions, visibility conditions, light conditions,shadow, blinding and others.

Finally, the vehicle forwards the third data sets to the serverdatabase. Communication with the server database may be done accordingto the availability of a communication network. So communication may,for example, be done when a vehicle is parked or stops at a trafficlight and Wi-Fi is available. Important and/or urgent data may always beexchanged via a mobile communications network, while other data may beexchanged at a later time when Wi-Fi is available. As the third datasets mainly comprise object data of driving-relevant objects, they arecomparatively compact and require less size than images taken from theroad or the objects. This significantly simplifies communication andreduces network load. Only in very special cases, larger image data maybe transferred to the server database. For example, if a traffic sign isnot readable, an image of this traffic sign may be forwarded to theserver database for further evaluation.

The steps mentioned above may either be performed in sequence,processing a certain quantity of data sets step by step or by processingindividual data sets. For example, data of a certain section of thevehicle's path may be collected and processed step by step after thepath section has been passed by the vehicle. Instead, data processingmay be made continuously while the vehicle is passing a certain sectionof its path. Furthermore, data may be continuously forwarded to theserver database or in bursts. Also a combination may be possible. Forexample there may be a continuous real-time processing when the vehicleis passing a certain section of its path, while recording all relevantsensor data, allowing a view back as described above, if the vehicledetects an important object.

Preferably, a selection of the received third data sets are stored bythe server database. Alternatively, all received third data sets arestored by the server database. Preferably, there is a large number ofthird data sets for each road stored in the server database. This may belike a history showing the results of previous third data sets fromdifferent vehicles for the road. The server database has at least fourthand third data sets available for further evaluation. The serverdatabase or a database manager which may be part thereof may review thethird data sets received from the vehicles about differences to thefourth data sets. The server database tries to fit the third data setsto the fourth data sets. Normally, the location information of theobjects of third data sets should be in close proximity to the fourthdata sets. There may be a deviation in an order of the maximum GNSStolerance defining a maximum tolerance window. Therefore, the serverdatabase only has to compare data sets within this maximum tolerancewindow. This processing performed by the server database is the secondobject data processing step for generating and updating a precision roadproperty database. It may further be complemented by the following stepsand methods.

The database preferably generates an object reference point for objects.Such object reference points may later be used for aligning objects orgroups of objects. The object reference point preferably is assigned toa part or feature of an object, which may be identified easily and withcomparatively high precision. It may for example be the geometric centreof a traffic sign or a specific light of a traffic light.

Preferably, the server database makes a statistical evaluation of theinformation received from the vehicles. If a significant number ofvehicles report the same differences, there is a certain probability,that the real road has changed compared to the server database.Therefore, the server database may be updated.

A very important aspect is improving the object description accuracy ofthe third data sets when generating the fourth data sets. For examplethe object modelling in the third data sets as done by the vehiclesmight not be optimal for the fourth data sets and may be changed by theserver database when the fourth data sets are generated. Also, GNSSposition information is prone to errors, not all of which have a normaldistribution, which could be dealt with by averaging. For example, afirst number of third data sets originating from a first vehicle mayhave a certain position offset over a second number of third data setsoriginating from a second vehicle. Or, remaining errors in the thirddata sets, resulting from a lack of calibration and normalisation of thevehicle sensors, such as optical distortions, need to be dealt with. Inorder to improve the object description accuracy, the information, andespecially the position information of a plurality of data sets,preferably third data sets, may be evaluated and/or aligned.

A preferred embodiment relates to a method of precision aligning ofvehicle path data as follows to improve the collected data by matchingand aligning certain collections of data related to the same part of theroad. Preferably, the server database receives third data sets from aplurality of vehicles which are based on at least one ambient data setrelated to each of the sections of the vehicle path, which the vehicledecides to report. Such ambient data sets preferably relate to at leastone of driving-relevant objects, road furniture and geographic objects.For generating such third data sets, object data processing is performedin the vehicle by evaluating the at least one ambient data sets andgenerating at least one second data set of driving-relevant object databased on the evaluation result and/or partial ambient data and/orintermediate processing results, and by comparing the at least onesecond data set of driving-relevant object data with at least one firstdata set of driving-relevant object data as stored in the vehicledatabase.

The server database identifies a first plurality of vehicle pathsections related to a part of the road at least partially overlappingeach other, each based on at least one third data set. There may be apreselection of such data sets based on location information about thedata set, which may be a GNSS position information. This is followed bya first coarse alignment step of the third data sets in the firstplurality of vehicle path sections relating to the same part of the roadbased on the location information contained in the at least one thirddata sets in each vehicle path section. For precision alignment, aplurality of object reference points and/or the related objects isidentified in at least two vehicle path section. The reference pointsand/or the related objects are grouped such, that at least two vehiclepath sections of the first plurality of the vehicle path sectionscomprise at least one of the same group of object reference pointsand/or the related objects. Finally, alignment functions for matchingand/or aligning the at least one group of object reference points and/orthe related objects in the at least two vehicle path sections of thefirst plurality of vehicle path sections relating to the same part ofthe road are generated or calculated. The same alignment functions areapplied to at least a selection of the other third data sets of the sameat least two vehicle path sections for generating or updating fourthdata sets containing driving-relevant object data. Such alignmentfunctions may comprise any of a linear translation and/or a rotation oreven a more complex transformation.

It is further preferred that the server database uses fourth data setsfrom the server database together with the third data sets as a basisfor alignment.

After generating and applying the alignment functions, the third datasets may be statistically processed by preferably calculating averagevalues and/or standard deviations of the aligned third data sets.

It is also preferred to do additional alignment and/or alignment byusing reference points at the road intersections and/or positions knownfrom satellite images and/or fixed geographical reference points.

According to a further embodiment, a server database has the necessarymeans to perform the method of precision alignment of vehicle path datarelated to the same part of the road. Preferably, the method isperformed by a computer having a computer program in memory.

The same method may be performed by a vehicle to improve accuracy of thesecond data sets, using the first and second data sets available in thevehicle. But it is preferred to perform the precision alignment ofvehicle path data related to the same part of the road within the serverdatabase instead of the vehicle, as the server database has asignificantly higher processing power and further has access to a largenumber of data provided by a large number of vehicles.

But even then, grouping may be performed by the vehicle for anotherpurpose. Grouping of object reference points and/or the related objectsmay be beneficial for precise vehicle positioning. Along roads, theremay be a significant number of objects with relative location referenceslike centreline dashes. As there are many dashes which essentially lookalike, it would be difficult to distinguish between the individualdashes and to get an absolute location reference longitudinally alongthe road. Anyway, even so, the dashes may be useful for determining theposition of the vehicle orthogonally to the road, relative to the roadborders. For verifying and correcting GNSS position information and/orfor precise positioning of the vehicle relative to the objects of theroad and also longitudinally along the road, further position referencesare required. Such position references may be unique objects or objectswhich rarely occur. Such references may be traffic signs or specialmarks on or besides the road. But special objects might not occur veryoften and not often enough for precise and continuous vehiclepositioning. Therefore it is further preferred that a plurality ofobjects is grouped together. Even if the individual types of objects inthe group may have many occurrences along the road and are not verydistinguishable, such a group of objects, and especially theconstellation it forms in 3D space may be unique for a long section ofthe road. An example is the constellation that two dashes form with thepole of a fence. Their position relative to each other has not beenplanned and therefore is very likely to be unique. Grouping of objectsor object reference points allows to determine the vehicle's preciseposition on the road even if no really unique individual objects can beidentified. The vehicle's precise position may be expressed as an offsetto the position provided by a GNSS system. It may further be expressedby a position value within the position-world of the first data sets,meaning in relation to the driving-relevant objects and/or groups ofdriving-relevant objects as contained in the first data sets.

The following method describes a preferred embodiment of precise vehiclepositioning. Preferably, the object reference points are identifiedwithin the first data sets. While driving, the vehicle generates seconddata sets from collected ambient data sets. In the second data sets, thevehicle tries to identify the same objects and/or object referencepoints and preferably groups of objects and/or object reference pointsas are contained in the first data sets. A vehicle's second data setswill usually only contain a selection of the data contained in the firstdata sets. By aligning the same objects or object reference points ofpreferably groups of objects and/or object reference points between thesecond data sets and the first data sets, the vehicle can derive aposition value for its own position and this way position itselfprecisely on the road.

According to a further embodiment, the first, second, third and fourthdata sets may have attached further information, for example aconfidence level, providing information about the confidence orreliability of the information. If a high number of vehicles reportedthe same differences, the confidence level is high. If only one vehicleor a low number of vehicles reported the difference, the confidencelevel is low. The confidence level may also be dependent on the vehiclegenerated confidence level. Therefore, data sets with a higher vehiclegenerated confidence level may have a higher weighing factor. Anotherfactor may be the sensor equipment grade of a vehicle. A better equippedvehicle may have a higher weight than a lower equipped vehicle whendetermining the confidence level. Generally, two different confidencelevel values like a high level and a low level may be sufficient. It maybe desirable to have a higher number of confidence levels like threelevels or nine levels which allow more detailed distinguishing. If asignificant new information has been reported the first time by avehicle, the confidence level may be low, but it may be an important orurgent information. The database manager therefore preferably generatesat least a further tag, which may indicate the importance and/or urgencyof an information. Alternatively, the confidence level may be set to ahigher value to indicate a higher importance or urgency of theinformation. For example, a dynamically changed speed limit may be anurgent information, as a vehicle driving exceeding the limit would besubject to prosecution. Whereas a change in road marking may be lessurgent, as the process of changing road markings takes hours or days,but it may also be an important information. Construction of a newbuilding besides the road would neither be important nor urgent. Buthaving information about the building may increase positioning accuracy.Generally, two different importance and/or urgency levels like a highlevel and a low level may be sufficient. It may be desirable to have ahigher number of levels like three levels or nine levels which allowmore detailed distinguishing.

If the server database knows that certain changes occur often at certainlocations, for example there may be frequent speed changes at a certainlocation due to an electronic speed display, it may assign a higherconfidence level. Furthermore, the vehicles preferably also provide avehicle generated confidence level of difference information. The serverdatabase may further alter the confidence level based on knowledge aboutthe vehicle or the environment, like the sensor quality, for example, ifit is a low resolution or a high-resolution camera, and the data qualityitself. In another example, a clear image with a high-resolution cameraon a clear day would provide a higher confidence level than a lowresolution image during a snowstorm.

Generally, data of further data providers like digital maps, satelliteimages or traffic information may be used to increase position precisionof the data sets and/or to complement the data sets.

A further embodiment relates to a method which uses the first data setsto reduce the overall image processing load in a vehicle and to extendthe usage range of highly automated driving (HAD) and other vehiclecontrol applications, for example towards bad weather conditions.

Vehicle control applications such as HAD heavily rely on processing ofimage data from image sensors. Image processing requires a lot ofprocessing power in a vehicle. Each type of image sensors typically hasa limited usage range and its specific strengths and weaknesses. Forexample cameras provide high resolution images in colour but have a lowdynamic range. They have difficulties with scenes with bright and darkareas. Cameras also don't work well in fog or rain. Radar, on the otherhand, functions well under those conditions, but only provides lowresolution monochrome images with reflectivity information. In order tocompensate the weaknesses of the different types of sensors, varioussensor types and multiple image sensors are combined in a vehicle toextend the usage range of HAD and other vehicle control applications.The need to evaluate and fuse the image data from multiple imagesensors, results in a lot of processing power needed. Nonetheless, theusage range of HAD and other vehicle control applications is ratherrestricted because bad weather conditions still impose challengesdespite expensive sensor equipment.

As described above, the method of generating and updating the precisionroad database adds additional image processing needs in the vehicle inorder to process the ambient data sets, to generate second data sets, tocompare second data sets with first data sets and to generate third datasets which are sent to the server database. Even more processing poweris added for precision vehicle positioning by comparing the ambient datasets to the first data sets. At least the latter must always work inreal-time at vehicle speed.

Preferably, this embodiment comprises two phases of processing and usageof data sets in vehicles. In a first phase, confidence in the fourthdata sets is built up by the server database collecting third data setsof sections of the vehicles' paths and evaluating them to generate thefourth data sets which further serve as a basis for the first data sets.Already in this first phase the first data sets may be provided tovehicles by the server database and used by the vehicles in thegeneration of the second data sets for guiding the vehicle-sideprocessing on the modelling approach of driving-relevant objects. But itis preferred if, in this first phase with lack of confidence in theobject data, first data sets with low confidence level are not used bythe vehicles for the processing of ambient data sets.

In a second phase, at least one of the first data sets generated fromfourth data sets has reached a high confidence level because asufficient number of third data sets have been collected by the serverdatabase in order to evaluate them and generate fourth data sets withprecision results and with a high level of confidence. Out of the fourthdata sets, the server database generates first data sets and sends themto the vehicles. First data sets with a high confidence level may beused for the processing of ambient data sets and for vehicle controlapplications. The type of use may be determined by the confidence levelvalue. For example first data sets with a very high confidence level maybe used for highly automated driving in fog while this function isde-activated if the confidence level of the respective first data setsis not very high. Driving in light rain, on the other hand, where thecameras ‘see’ more, may still be allowed if the confidence level in thefirst data sets is high but not very high. The limit value may beadapted to environmental and/or driving conditions.

In the following, the first phase and the second phase are explained indetail.

Preferably, in the first phase of building up confidence, the processingload on a vehicle is limited by sampling ambient data sets for a segmentof the road and by processing the resulting sequence of ambient datasets off-line, meaning not following the vehicle at its speed by leavingsegments of the road out between segments of the road that areprocessed. This restriction is not a disadvantage for the collection ofthird data sets by the server database. The collection of third datasets can rely on many vehicles providing third data sets for segments oftheir path. It is not necessary that one vehicle provides completeinformation about its complete path. Each vehicle can sample and store asequence of ambient data sets and process it off-line with as muchprocessing power as it has and take as long as it needs for that. Ifthat vehicle misses parts of its path in between segments of its paththat it processes, these gaps will be filled by other vehicles.

The method of generating second data sets and/or information for vehiclecontrol and/or information for vehicle positioning and/or storing ofsequences of ambient data sets and off-line processing has variousoptions, which mostly have been described before but are summarizedbelow. Any one or a plurality of these options may be selected tocomplement off-line processing:

-   -   Zoom option: Selecting an ambient data set, which has been taken        close to an object to be processed and shows it large and clear        for good recognition results. More distant ambient data sets may        be used for step-wise triangulation and positioning of the        object relative to other objects. This results in good        recognition and precise positioning results with lean processing        algorithms.    -   Close-up option: Selecting an ambient data set which is close to        the object of interest, in each ambient data set. As only the        near-field is of interest, where recognition and relative        measurements work well and/or lean processing algorithms can be        used.    -   Moving back and forth option: Moving back and forth through the        ambient data sets in order to determine objects of interest from        a close distance or from different viewing angles and distances        and thus with lower processing power.    -   Sequence option: Running a sequence of ambient data sets in        series through different sets of algorithms. It is not necessary        to process and recognise all objects in parallel as real-time        processing would require. This increases the possibilities for        applied overall processing while reducing the requirement on        processing power significantly.    -   Review option: Processing backwards from the latest ambient data        set into the past. In contrast to real-time processing, as for        example the HAD control application does, there is no need to        look forward from the latest ambient data set into its far-field        and thus into the future of what will be coming towards the        vehicle. Also this results in lean algorithms and low processing        power needed.

Overall, the fact that off-line processing of sequences of ambient datasets is possible in this first phase, results in good processing resultswith lean algorithms and a low burden on the processing power needed.

It is also preferred if the generation of third data sets in this firstphase mainly is done if the processing conditions are good, meaning atgood weather and in good light conditions. Not having to generate thirddata sets under bad conditions, leads to being able to mainly rely oncameras only as image sensors for the ambient data sets and lowprocessing power needed, even for good processing results. Becauseprocessing of ambient data sets can be done under good conditions and noreal-time processing is needed in this first phase, the availableprocessing power can be used for generation of third data sets with manydriving-relevant objects, fully populated data and precision results asis needed in this first phase for good third data sets for segments ofthe vehicles' paths.

In the second phase, at least one of the first data sets generated fromfourth data sets has reached a high confidence level. There may still befurther first data sets having a low confidence level. First data setswith a high confidence level just need to be probed and confirmed byvehicles passing the respective objects. It is preferred if vehiclesactively confirm the data in the first data sets within the third datasets until a very high confidence level has been reached. It ispreferred if the vehicles mainly report their path driven only withinthird data sets, once the data in the first data sets has reached a veryhigh confidence level. By reporting their path driven without reportinga difference, the vehicles indirectly confirm the data of the first datasets along the path. This reduces communication volume. It is alsopreferred if the path driven information of a vehicle is neutralizedsuch that it cannot be tracked down to the driver and raise privacyconcerns. It is preferred if the server database monitors how manyvehicles and when have directly or indirectly confirmed which parts ofdata in first data sets. If there have not been enough confirmations, itis preferred for the server database to change the confidence level ofthe respective data in the fourth data sets to a respectively lowervalue.

If a vehicle could only confirm parts of first data sets along its pathactively or indirectly or report differences, the server database wouldneed to track, which vehicle confirmed which part of data of first datasets. For this, the server database would need to know morespecifically, which vehicle moved on which path at what time. This maycause privacy concerns. In order to avoid that, each vehicle preferablyis able to confirm all first data sets along its path or to detectdifferences. This requirement means real-time processing of all ambientdata sets, at least to a certain extent.

In order to realize that with low burden on the processing power, it ispreferred that, from a high confidence level on, first data sets areused to support the processing of ambient data sets. This support may beprovided for the generation of driving-relevant objects for the seconddata sets and it may be provided by two fundamentally different methods:

-   -   Method 1: As described before, the first data sets comprise        object descriptions of the driving-relevant objects of the empty        road. It can be used to quickly detect and check for the same        objects in the ambient data sets. However, for this, the ambient        data sets have to be processed at least up to a certain object        level first, before such comparison is possible. If, for        example, a stop sign is partly covered with snow, it is most        likely not possible to recognize it as an object for comparison        with the object data of the first data sets.    -   Method 2: As described before, the first data sets, preferably,        comprise a vector graphics format, which allows rendering as an        image in different ways and from different viewing angles. For        each of the images in an ambient data set, a respective image of        the empty road or parts thereof can be rendered, which shows the        view of the empty road and of the driving-relevant objects in        the expected places and in the way a respective sensor ‘sees’        the empty road. This allows comparison already on image level.        For example to compare an image from a camera sensor, a        respective image can be rendered from the first data sets in the        way, also humans see and from the viewing angle of the        respective camera. For an image from a radar sensor, a        respective image can be rendered from the first data sets in the        way, a radar sensor sees the world, with reflectivity and        run-time information. Comparing ambient data sets already on an        image level to first data sets, allows to reach the right        conclusions faster and with a higher level of certainty and/or        with lower processing power. The partly snow-covered stop sign        can now be recognized by comparing the ambient data sets already        on an image level to images of the empty road under good        conditions and by identifying the shape of the stop sign in the        right place and maybe some blob of red colour, where a part of        the sign is not covered by snow. Method 2 helps object        recognition in a similar way as humans do image processing by        using images generated from the first data sets as a kind of        experience ground-truth for comparison.

Method 1 and method 2 may also be used in a mixed form. For example,first, the object description of the empty road in the first data setsmay be checked for the driving-relevant objects to be detected, before acomparison on image level takes place for detailed and fast detection.The processing may also jump back and forth between object descriptionand image representation of the empty road to iterate towards goodprocessing results.

In this way, the first data sets may be used to support the generationof the second data sets. This lowers the necessary processing power andallows the processing to follow the vehicle at speed, without gaps, inorder to confirm the complete content of the first data sets along thevehicle's path to the server database or to report differences betweenfirst and second data sets in third data sets. The task of ambient dataset processing moves from segment processing in the first phase to atleast partial real-time processing in the second phase.

In addition to the update of the server database in third data sets,first data sets may also be used to support the evaluation of theambient data sets and comparison to the first data sets for precisionvehicle positioning, which also is a real-time task. Objects and/orobject reference points and/or groups of objects and/or groups of objectreference points can be identified and compared quickly and/or withlower processing power that without the help of the first data sets.

In the same way, the first data sets can also support the real-timeprocessing of ambient data sets done by the HAD function or othervehicle control applications. This reduces the necessary processingpower in order to do HAD, precise vehicle positioning and update of theprecision road database in parallel.

The support of image processing by the first data sets, especiallythrough method 2, also increases the usage range of HAD and othervehicle control applications towards bad weather conditions. Methods 1and 2 for support of ambient data processing through the first data setscan also be used for the vehicle control applications. The example ofthe partly snow-covered stop-sign was discussed before. Another exampleis automated driving at night time. The cameras only ‘see’ as far as theheadlights shine. Radar may see further, but is not able to ‘see’ theroad markings. First data sets can help to ‘see’ the road markingsfurther ahead of the vehicle by providing an expectation where they willbe or go as soon as the camera starts to identify them at the edge ofits range of vision. These were only a few examples of a broad varietyof possibilities how first data sets help HAD and other vehicle controlapplications.

It is obvious that the sequence of some of these steps may be changed,assumed that any of the previous steps has provided data required in alater step.

The embodiments described above relate to a method for generating andupdating a precision road property database comprising driving-relevantobjects by using a vehicle, a server database handling, processing,storing data sets comprising driving-relevant road object data, andcommunicating with a vehicle, as well as a vehicle having means forcollecting driving-relevant data, evaluating such data and communicatingwith the server database. Preferably, the method is performed by acomputer or a plurality of computers having a computer program inmemory.

DESCRIPTION OF DRAWINGS

In the following, the invention will be described by way of example,without limitation of the general inventive concept, on examples ofembodiment with reference to the drawings.

FIG. 1 shows a general scenario with self-driving vehicles driving onroads.

FIG. 2 shows basic vehicle components.

FIG. 3 shows a precision vehicle position detection.

FIG. 4 shows road property data updating.

FIG. 5 shows basic ambient data set acquisition.

FIGS. 6a, 6b and 6c show different images taken by a vehicle proceedingalong a road.

FIG. 7 shows a full ambient data set acquisition.

FIG. 8 shows an example of grouping of objects.

FIGS. 9a and 9b show a first example of matching by grouping objects.

FIGS. 10a and 10b show a second example of matching by grouping objects.

FIG. 11 shows the basic flow of data collection.

FIG. 12 shows a detailed flow diagram of precision alignment of vehiclepath data related to the same part of the road by the vehicle

FIG. 13 shows a detailed flow diagram of precision alignment of vehiclepath data related to the same part of the road by the server database.

FIG. 14 shows a detailed flow diagram of precision positioning.

FIG. 15 shows the basic 2-Phase method of building up a road database.

FIG. 16 shows phase 1 in detail.

FIG. 17 shows phase 2 in detail.

FIG. 18 shows an image of an exemplary road.

FIG. 19 shows the driving relevant objects of FIG. 18.

FIG. 20 shows an image rendered from objects.

In FIG. 1, a general scenario with self-driving vehicles driving onroads is shown. A road network 100 comprises a plurality of roads 101.The roads have specific properties like the width, the direction, thecurvature, the number of lanes in each direction, the width of lanes, orthe surface structure. There may be further specific details like a curbstone, a centreline, even a single dash of a dashed centreline or othermarkings like a crosswalk. Close to the roads are driving-relevantgeographic objects 140. A geographic object may be any stationary objectlike but not limited to a building, a tree, a river, a lake or amountain. Furthermore, there is plurality of road furniture objects 150which may comprise objects and pieces of equipment installed on streetsand roads for various purposes, such as benches, traffic barriers,bollards, post boxes, phone boxes, streetlamps, traffic lights, trafficsigns, bus stops, tram stops, taxi stands, public sculptures, and wastereceptacles. There may be further objects which are not falling into oneof the above categories, but are also relevant for driving. Such objectsmay be green-belts, trees, stones, walls or other obstacles close to aroad. They may also comprise structures like trenches, plane surfacesand others which may be considered for planning alternate, emergencyexit or collision avoidance paths.

Furthermore a service provider 120 preferably has at least a databasehub 121 connected to a server database 122. For the communicationbetween the service provider 120 and the vehicles 110, a communicationnetwork 130 is provided. Such a communication network may be a mobilecommunications network, Wi-Fi or any other type of network. Preferably,this network is based on an Internet protocol. The embodiments generallyrelate to vehicles, which may be cars, trucks, motorbikes or any othermeans for traveling on a road. For simplicity in the figures cars areshown.

In FIG. 2, basic vehicle components 200 are shown. Preferably a vehiclecomprises at least one of environmental sensors 210, vehicle statussensors 220, position sensors 230 and a communication system 240. Theseare preferably connected by at least one communication bus 259 to aprocessor system 250. This communication bus may comprise a single busor a plurality of buses. The processor system 250 may comprise aplurality of individual processors 251 which preferably are connected toeach other by a bus 252. Furthermore they are preferably connected to avehicle database or storage 253.

Environmental sensors 210 collect information about the environment ofthe vehicle. They may comprise a camera system like a CCD camera whichmay be suitable for capturing visible and/or infrared images. Preferablya simple mono-camera is provided. Alternatively, a stereo camera, whichmay have two imaging sensors mounted distant from each other may beused. There may be further sensors like at least one radar sensor or atleast one laser sensor or at least one RF channel sensor or at least oneinfrared sensor or another environmental sensor. The sensors may be usedfor scanning and detecting outside objects.

The status sensors 220 collect information about the vehicle and itsinternal states. Such sensors may detect the status of driving, drivingspeed and steering direction.

The position sensors 230 collect information about the vehicle'sposition. Such position sensors preferably comprise a GNSS system. Anypositioning system like GPS, GLONASS or Galileo may be used. Herein onlythe term GPS or GNSS is used indicating any positioning system. Theremay further be a Gyro, a yaw sensor and/or an accelerometer fordetermining movements of the vehicle. It is further preferred to have awheel dependent distance sensor like a rotation sensor which may be usedfor further systems like antilocking systems or Anti Blocking System(ABS) for measuring driven distances and/or a steering sensor fordetecting driving direction. There may be further sensors like analtimeter or other altitude or slope sensors for precision detection ofaltitude changes. The position sensors 230 may at least partiallyinteract with or use signals from the status sensors 220.

The communication system 240 is used for communication with devicesoutside of the vehicle. Preferably, the communication system is based ona communication network, which may be a mobile communications network,Wi-Fi or any other type of network. The communication system may usedifferent communication networks and/or communication protocolsdependent on their availability. For example in a parking position ofthe vehicle Wi-Fi may be available. Therefore in such positions Wi-Fimay be the preferred communication network. Furthermore, Wi-Fi may bemade available at intersections, close to traffic lights and close toroad sections with low traffic speeds or regular congestion. If Wi-Fi isnot available, any other communication network like a mobilecommunications network may be used for communication. Furthermore, thecommunication system 240 may have a buffer to delay communication untila suitable network is available. For example, road property data whichhas been allocated during driving may be forwarded to a serviceprovider, when the vehicle has arrived in a parking position and Wi-Fiis available.

The processor system 250 may be a processor system which is alreadyavailable in a large number of vehicles. In modern vehicles, manyprocessors are used for controlling different tasks like enginemanagement, driving control, automated driver assistance, navigation,and entertainment. Generally, there is significant processor poweralready available. Such already available processors or additionalprocessors may be used for performing the tasks described herein. Theprocessors preferably are connected by a bus system which may comprise aplurality of different buses like CAN, MOST, Ethernet or FlexRay.

In FIG. 3, the basic data flow of precision vehicle position detectionis shown. The position accuracy which may be achieved by positioningsystems like GNSS is not sufficient for self-driving vehicles. Astandard position tolerance of 10 m is more than the width of normalroads. To enable self-driving it is necessary to achieve a much moreprecise determination of a vehicle position.

As a starting point preferably the best estimate of the vehicle'sposition 501 is used. This may for example be a GNSS determinedposition. It may also be the last known position of the vehicle. Thisinformation is forwarded to a vehicle database 253 which contains alocal environment description 504. This local environment descriptionmay contain data about the road, like but not limited to the roadfurniture and/or the geographic environment. This data is correlated 505preferably in a data correlator unit with environmental sensor data 502which may have been acquired by environmental sensors 210. Furtherpreferably, position sensor data 503 which may have been acquired by thevehicle position sensors 230 may be used for further correlation. By atleast correlating the local environment description 504 withenvironmental sensor data 502 an updated vehicle position information506 may be obtained.

In FIG. 4, the data flow of road property data difference detection isshown. Again, as a starting point preferably the best estimate of thevehicle's position 501 may be used. This may for example be a GNSSdetermined position. It may also be the last known position of thevehicle or an updated vehicle position from step 506. This informationis forwarded to a vehicle database 253 which preferably delivers atleast road property data 513, road furniture data 514 and environmentaldata 515. Furthermore, environmental sensor data 512 may be acquired byenvironmental sensors 210. Preferably, the environmental sensor datapasses a specific data processing or image processing 523 to generatedata required for the data correlation steps. Road property datacorrelation 516 using road property data 513 of the vehicle database andenvironmental sensor data 512 preferably generates data containing roadproperty data differences 519. Road furniture data correlation 517 usingroad furniture data 514 of the vehicle database and environmental sensordata 512 preferably generates data containing road furniture datadifferences 520. Furthermore, environmental data correlation 518 usingenvironmental data 515 of the vehicle database and environmental sensordata 512 preferably generates data containing environmental datadifferences 521. The generated difference data may contain more or lessinformation depending on the amount of differences detected. For thecase, no differences have been detected, the differences may contain nofurther information or only a marker indicating that that no furtherinformation is available. For the case, the vehicle database 253 did notdeliver any specific data like roads data 513, road furniture data 514or environmental data 515, the differences may contain a full data setas provided by the environmental sensor data 512. Alternatively, theenvironmental sensor data is further compressed by a data compressor orcompression procedure in a computer. Upon request by the serverdatabase, the vehicle may provide full data sets or even additional datalike images or partial images.

In FIG. 5 basic acquisition of an ambient data set andgeneration/evaluation of road objects is shown in more detail togetherwith FIGS. 6a, 6b and 6c . Generally, the term ambient data set is usedherein for a data set generated by a plurality of sensors which maycomprise at least one optical image sensor and/or radar sensor and/orinfrared sensor and/or other environmental sensor. From the combinationand/or correlation of these sensor signals, an ambient data set from theperspective of a vehicle is collected, out of which a set of roadobjects is generated. A vehicle 110 is driving on a road 300. The roadhas the right limit 310 and the left limit 311 which may be marked bylimit lines, a curb or any other limitation or marking means. At thecentre of the road there is a centreline which is a dashed line in thisembodiment, having a plurality of dashes 320, 330, 340, 350 and 360.Each dash has a beginning and an end. The beginnings are marked 321,331, 341, and 351 whereas the ends are marked 322, 332, 342 and 352.There may be any other type of centreline or even no centreline.Furthermore, there is some road furniture, which may be traffic signs370, 371, 372. From the captured ambient data set the vehicle preferablyidentifies the above mentioned road objects and most preferablygenerates respective second data sets.

Although an ambient data set may give a 360 degree representation of thevehicle's environment, here a more limited view is shown by referring toa viewing sector 380 which may correspond to a front camera, having anapproximately 90° viewing angle. The corresponding front image is shownin FIG. 6a . Here only part of the road objects, the right limit 310 andthe traffic sign 380 can be seen at the right side of the image. Theleft side of the image shows centreline dash 330 and part of thefollowing centreline dash 340. It is obvious, that this view gives aclear image of the road objects related to the right lane of the road,where the vehicle is driving, but cannot provide too much informationabout the road objects at the left lane from which parts can be seen inthe left top corner of the image. When the vehicle proceeds along theroad through positions 111 and 112, it captures images according to theimage sectors 381 and 382. The corresponding images together with theidentified road objects are shown in FIGS. 6b and 6 c.

Matching this sequence of road objects will give a continuousrepresentation of the road. This matching cannot be compared to imagestitching which is known from panorama cameras or panorama software.There, only matching marks in adjacent images have to be identified andthe images must be scaled accordingly. For matching of road objects aspatial transformation of the driving-relevant object data sets must bedone. Such a transformation may be a linear displacement, a scaling oreven a complex nonlinear transformation to align captured data. Detailsof such transformation will be explained later. These transformationswill be partially facilitated by the road objects themselves, which mayallow automatic recalibration of sensors when consistent errors arefound between detected road objects in second data sets and first datasets. In FIG. 7, an acquisition and evaluation of a driving-relevantobject data set for a full road is shown. As explained with respect toFIG. 5, a first set of ambient data 380, 381 and 382 is captured by afirst vehicle propagating in a first direction on the right lane whichis from the right to the left on the top lane in this figure, andconverted to driving-relevant object data sets. A second set of ambientdata is captured by a second vehicle propagating in the oppositedirection on the left lane which is from the left to the right on thebottom lane in this figure, and converted to driving-relevant objectdata sets. The driving-relevant object data sets generated by the secondvehicle may be similar as shown in FIGS. 6a, 6b and 6c . The maindifference is that the driving-relevant object data sets now mainlycontain the road objects at the second lane and therefore provideinformation about the second lane. So far, none of the vehicles has afull set of the road objects of the full road, but the server databasemay fit together the information provided by the vehicles to get thefull set of all road objects provided so far. For this purpose, thevehicles generate object-based information about the individual roadobjects like right limit 310, left limit 311, centreline dashes 320,330, 340, 350, 360 and traffic signs 370, 371, 372. This object-basedinformation is collected and evaluated by the server database togenerate a full driving-relevant object data set of the road in thesecond object data processing step of the two-step processing forgenerating and updating a precision road property database. This exampleis not limited to two vehicles, but may be extended to any number ofvehicles. It is further not required that a single vehicle provides acontinuous stream of driving-relevant object data sets. For example, theobject-based information relating to the first ambient data set as shownin FIG. 6a may be provided by a first vehicle, whereas the object-basedinformation relating to the second ambient data set of FIG. 6b may beprovided by a second vehicle, passing the same road at a later time, andthe third ambient data set of FIG. 6c may be captured by a thirdvehicle, again passing the same road later.

In FIG. 8 an example of grouping of objects is shown. Grouping ofobjects may be used to provide a simplified and better matching ofadjacent ambient data sets by the vehicle or of driving-relevant objectdata sets by the vehicle or by the server database. Basically, a vehiclemay use grouping for the first object data processing step. Mostpreferably, the server database uses grouping for the second object dataprocessing step.

Along roads, there may be a significant number of relative locationreferences like centreline dashes. As there are many dashes whichessentially look alike, it would be difficult to distinguish between theindividual dashes and to get an absolute location reference. Anyway, thedashes may be useful for determining the position of the vehiclerelative to the road. For verifying and correcting GNSS positioninformation, further absolute position references are required. Suchposition references may be objects which rarely occur or which at leastoccur only once within a GNSS tolerance interval. Such references may betraffic signs, gullies, man-holes or just characteristic markings on theroad. They may be grouped together with road markings like dashedcentrelines. A first group 401 comprises centreline dash 350 and trafficsign 372. A second group 402 comprises the centreline dash 330 and thetraffic sign 370 as well as the traffic sign 371 at the opposite side ofthe road. By referencing to such groups of objects, it is possible tocalculate the correct transformation of driving-relevant object datasets or further sensor data having the group or at least parts thereof.Such a transformation may be a linear displacement, a scaling or even acomplex nonlinear transformation to match a captured driving-relevantobject data set. This allows a better matching of driving-relevantobject data sets being taken of the road, even if there are errors inthe three-dimensional conversion of the taken images or measuredparameters from which the driving-relevant object data sets of a vehiclehave been generated. Consequently, the precision of the generated objectmodels is improved. For example, if a driving-relevant object data setsis available of the second vehicle showing group 401, the traffic sign372 would only be at the outmost corner of the correspondingdriving-relevant object data set of the vehicle driving in the oppositedirection, but the corresponding centreline dash may also be wellidentified by the vehicle driving in the opposite direction. This isfurther shown in more detail in the next figures.

In FIGS. 9a and 9b a first example of matching by grouping objects isshown. FIG. 9a shows the view from a vehicle at the bottom lane of FIG.5 passing from the left to the right. Here, the traffic sign 372 is wellvisible and can easily be grouped together with centreline dash 350 toform a first group of objects 401. As the traffic sign is in the mainfield of view, its position can be determined precisely. Furthermore,the relative position of centreline dash 350 can also be determinedprecisely. FIG. 9 b shows the view from a vehicle on the top lanepassing from the right to the left. Traffic sign 372 is only at theoutmost corner of the image and far away. By aligning the groupedobjects and/or by aligning the pattern formed by the grouped objectslike the triangle, an easy matching of driving-relevant object data setstaken by different vehicles driving in different directions can beachieved. Object grouping and matching may be improved by determiningcharacteristic points and/or edges of the objects. In this embodiment,the first end 351 and the second end 352 of the centreline dash 350 areused together with the base of traffic sign 372 to generate acharacteristic pattern of the first group of objects 401. Matching ofthis pattern allows precise fitting of individual driving-relevantobject data set pieces and/or object positions together to generate aprecise total object description. As in FIG. 9a and FIG. 9b basicallythe same triangular pattern is identified, the two driving-relevantobject data sets and/or object positions may easily be matched together,although they have been made from different views.

Furthermore, this matching may be used for precision positiondetermination. The vehicle driving from right to left in FIG. 5 cannotuse traffic sign 372 as it is in the outmost periphery of its view. Byidentifying the centreline dash 350 of the first group of objects 401 acomparatively precise location can be done. Based on the informationdetermined by a vehicle previously driving from left to right, thevehicle driving in the opposite direction knows the precise location ofcentreline dash 350 which is associated with traffic sign 372.

FIGS. 10a and 10b show a second example of matching by grouping objects.Here, a second group 402 of objects, comprising the centreline dash 330and the traffic sign 371 is shown. The contour of this group is definedby the first end 331 and the second end 332 of the centreline dash 330together with the base of traffic sign 370 and the base of traffic sign371. In FIG. 10a a first driving-relevant object data set generated by avehicle driving the road of FIG. 5 from right to left on the top lane isshown. Here, only part of the group 402 is shown. In FIG. 10b , adifferent driving-relevant object data set taken by a different vehicleand/or by a different camera is shown. This driving-relevant object dataset has been captured with image sensors with a larger viewing angle andtherefore also shows traffic sign 370. Here, all the members of thesecond group 402 of objects are shown. At least the first section ofgroup 402 comprising centreline dash 330 and traffic sign 371 can becorrelated with the previous driving-relevant object data set. Byreferring to the traffic sign 371 at the other side of the road furtheralignment with objects detected by a vehicle driving in the oppositedirection may be made.

In FIG. 11 the basic flow of data collection, distribution andprocessing is shown. Specifically the combination of the first objectdata processing step 555 in the vehicle and the second object dataprocessing step 559 in the server database result in data sets having ahigh accuracy and precise position information. According to the method,a precision road property database comprising driving-relevant objectsis updated by using a vehicle which is communicating with the serverdatabase. The figure has two columns. The left column shows the actionsrelated to the vehicle, while the right column shows actions related tothe server database. The server database preferably storesdriving-relevant object data. Such driving-relevant object data mayfurther comprise at least location information and detailed object-basedinformation about at least one of a road object, a road furnitureobject, a geographic object and a further object related to driving.

In a first step, the vehicle requests information 550 from the serverdatabase, for example based on a planned route or an area of interest.Alternatively, the server database may trigger a transfer 551, forexample for updating specific information. After such a request ortrigger, the server database generates 552 at least one first data setwhich is related to the request or trigger. These first data sets arebased on fourth data sets stored in the server database. Preferably,each first data set comprises driving-relevant object data. Preferably,the server database has stored information about the status of thevehicle database. Such information may comprise a copy of the data setsstored in the vehicle or at least one identifier for identifying datasets stored in the vehicle. This status information is preferablyupdated after the vehicle has confirmed reception of first data sets. Byusing this status information, the first data set may only compriseamended data sets and/or new data sets. There may be data sets which thevehicle does not have, data sets, which the vehicle has, but which areupdated, data sets, which the vehicle has, but which are retransmitted,or data sets which the vehicle should delete. Therefore, if requested bythe vehicle, the at least one first data set may contain data about therequired coverage. If triggered by the server database, the at least onefirst data set may contain updated data and/or warning data and/or datawhich should be verified by the vehicle. Preferably, each first data setcomprises at least one location information and detailed object-basedinformation. The location information specifies a position, an area orregion to which the detailed object based information is applicable.

In a next step, the first data sets are forwarded to the vehicle 553 andstored in the vehicle database. Following, the vehicle collects ambientdata 554 by at least one sensor as described above. This collection maytake place along at least a specific section of its path and/or at aspecific time and/or a specific object. Preferably, the ambient datasets are relating to at least one of driving-relevant objects, roadfurniture and geographic objects. Such ambient data is processed in thefirst object data processing step 555 to generate second data setscomprising object-based information. This processing may includestatistical evaluation based on the ambient data and/or at least one ofthe first data sets. It may further include grouping, matching andtransformation of data as described above. In a further step 556, thedifferences between the third and first data sets are preferablycalculated to generate third data sets containing differences betweenthe object-based information of the second data sets and theobject-based information of the first data sets related to the sectionof the vehicle path. Furthermore, at least one third data set maycomprise object-based information of at least one second data setinstead of a difference to a first data set. If necessary and/orrequested by the server database, such third data sets may also containnew data and/or data independently of existing first data sets. In anext step 557, the third data sets are forwarded to the server database.The server database stores the received third data sets 558 and startsprocessing them in the second object data processing step 559. Suchprocessing preferably comprises statistical evaluation and further postprocessing of the third data sets. Additionally, the fourth data setsstored in the server database may be used. Such post processing mayinclude object grouping, object matching, group matching,transformation, statistical calculations, and other processes. Later,the fourth data sets may be updated 560 by using the results thereof.

In FIG. 12 a detailed flow diagram of precision alignment of vehiclepath data related to the same part of the road as part of step 555 bythe vehicle is shown. In step 570 the vehicle identifies a firstplurality of vehicle path sections relating to the same part of the roadand at least partially overlapping each other based on at least onesecond data set. Then, in step 571, a plurality of object referencepoints is identified in each vehicle path section. A reference pointpreferably is a point which can easily and precisely be identified. Thereference points are grouped in step 572 such, that in at least two ofthe vehicle path sections of the first plurality of vehicle pathsections, each group comprises the same object reference points. Later,alignment functions for matching and/or aligning the at least one groupof object reference points in at least two of the vehicle path sectionsof the first plurality of vehicle path sections relating to the samepart of the road are generated or calculated in step 573. Such analignment function may comprise any of a linear translation and/or arotation or even a more complex transformation. The same alignmentfunctions are applied in step 574 to all other second data sets of thesame at least two vehicle path sections for generating or updatingfourth data sets containing driving-relevant object data.

After generating the alignment functions, the second data sets may bestatistically processed by preferably calculating average values and/orstandard deviations of the third or second data sets.

In FIG. 13 a detailed flow diagram of precision alignment of vehiclepath data related to the same part of the road by the server database aspart of step 559 is shown. In step 580 the server database identifies afirst plurality of vehicle path sections relating to the same part ofthe road and at least partially overlapping each other based on at leastone third data set. Then, in step 581, a plurality of object referencepoints is identified in each vehicle path section. A reference pointpreferably is a point which can easily and precisely be identified. Thereference points are grouped in step 582 such, that in at least two ofthe vehicle path sections of the first plurality of vehicle pathsections, each group comprises the same object reference points. Later,alignment functions for matching and/or aligning the at least one groupof object reference points in at least two of the vehicle path sectionsof the first plurality of vehicle path sections relating to the samepart of the road are generated or calculated in step 583. Such analignment function may comprise any of a linear translation and/or arotation or even a more complex transformation. The same alignmentfunctions are applied in step 584 to all other third data sets of thesame at least two vehicle path sections for generating or updatingfourth data sets containing driving-relevant object data.

After generating the alignment functions, the third data sets may bestatistically processed by preferably calculating average values and/orstandard deviations of the fourth or third data sets.

In FIG. 14, a detailed flow diagram of precision positioning is shown.Precision positioning of a vehicle is done by using data from a databaseand data collected by the vehicle when traveling along a path. In afirst step 590, the vehicle receives first data sets from the database,which are based on fourth data sets comprising driving-relevant objectdata. Such driving-relevant object data may comprise at least locationinformation, an optional object reference point and detailedobject-based information about at least one driving-relevant object likea road object, a road furniture object, a geographic object or a furtherobject related to driving. When driving, in step 591, the vehiclegenerates a plurality of ambient data sets along at least sections ofits path by at least one sensor of the vehicle. The ambient data setsrelate to at least one of driving-relevant objects, road furniture andgeographic objects. In step 592, the vehicle performs object dataprocessing by evaluating the at least one ambient data sets andgenerates at least one second data set of driving-relevant object databased on the evaluation results. In step 593, during driving and aftergenerating second data sets, the vehicle tries to identify in the seconddata sets the same driving-relevant objects and/or object referencepoints and/or groups of driving-relevant objects and/or object referencepoints as are contained in the first data sets. In step 594, the vehiclealigns the same driving-relevant objects and/or object reference pointsand/or groups of driving-relevant objects and/or object reference pointsbetween the second data sets and the first data sets, and calculates orderives a position value for its own position in step 595.

FIG. 15 shows the basic 2-Phase method of building up a road databasewhile reducing the overall image processing load in a vehicle andextending the usage range of highly automated driving (HAD) and othervehicle control applications. After step 553 of FIG. 11, a vehicle hasreceived first data sets in step 600. In the comparison step 601, theconfidence level of individual first data sets is compared with a limitvalue. If the confidence level exceeds the limit value, the secondphase, phase 2 is initiated for the first data set. Else phase 1 startswith said first data set.

FIG. 16 shows Phase 1 in more detail. Here, further ambient data iscollected in step 610 as may be shown in more detail in FIG. 4. Later,off-line data processing is started in step 611, using the ambient data.Although it is preferred, to process the collected ambient dataoff-line, e.g. after driving or when driving on a road segment where nodata is sampled for further processing, processing may be done on-lineif sufficient processing power is available. In step 612 a processingoption is selected. Such processing options have been disclosed in moredetail above and may comprise:

Zoom 613: Selecting an ambient data set, which has been taken close toan object together with more distant ambient data sets for step-wisetriangulation and positioning of the object relative to other objects.This results in good recognition and precise positioning results withlean processing algorithms.

Close-up 614: Selecting an ambient data set which is close to the objectof interest, in each ambient data set.

Moving back and forth 615: Moving back and forth through the ambientdata sets in order to determine objects of interest from a closedistance or from different viewing angles and distances.

Sequence 616: Running a sequence of ambient data sets in series throughdifferent sets of algorithms.

Review 617: Processing backwards from the latest ambient data set intothe past.

After selecting one or a plurality of these options, third data sets aregenerated in step 618.

FIG. 17 shows Phase 2 in more detail. First, ambient data are collectedin step 620. In step 621 the method of generation of driving-relevantobjects for the second data sets is selected. Steps 620 and 621 may beexchanged in their order. So either the method may be selected on thekind of ambient data or other parameters or the data may be collected asrequired by the method. The decision on the method is mainly based onthe type of first data sets. If the first data sets comprise objectdescriptions of the driving-relevant objects of the empty road, thefirst method is selected. If the first data sets comprise a vectorgraphics format, which allows rendering as an image in different waysand from different viewing angles, the second method is selected.

If method 1 has been selected, the object descriptions of the first datasets are processed. If method 2 has been selected, a comparison on imagelevel is made. Details of this method are disclosed above. By usingeither method, second data sets are generated 624 based on first datasets. Furthermore, both methods may be used for the same data sets.

In addition to the steps shown here, the first data sets may be probedand confirmed by the vehicle. Differences may be reported via third datasets.

The selection of the method may be made starting to collect ambientdata. In such a case the selector 621 is not required.

FIG. 18 shows an image of an exemplary road having a plurality ofdriving relevant objects.

FIG. 19 shows the driving relevant object in the previous image. Thereare roads 701-704, street markings 710-717, traffic lights 720 and 721and traffic signs 730-733. First data sets, which may be stored in avehicle preferably comprise driving-relevant object data. Suchdriving-relevant object data may further comprise at least locationinformation, an optional object reference point and detailedobject-based information about at least one driving-relevant object likea road object, a road furniture object, a geographic object or a furtherobject related to driving and data comprising a vector graphics format,which allows rendering as an image. A first data set may furthercomprise confidence level information.

FIG. 20 shows an image rendered from objects related to driving and datacomprising a vector graphics format of the first data sets preferablyrelating to the objects of the previous figure. Such rendered images maybe used for generating information for vehicle control and/orinformation for vehicle positioning and/or second data sets forgenerating and updating first data sets by comparing the at least oneambient data set on an image level to an image or partial image. Theambient data sets may have been previously collected along its path byat least one sensor of the vehicle, the ambient data sets preferablyrelate to at least one of driving-relevant objects, road furniture andgeographic objects. Alternatively, the information may be generated bycomparing the at least one ambient data set with the object descriptionsof the at least one first data set comprising object descriptions of thedriving-relevant objects of the empty road.

1. A method of generating and updating a precision road propertydatabase comprising driving-relevant objects by a two-step object dataprocessing comprising a first step of object data processing by avehicle and a second step of object data processing by a serverdatabase, the server database storing fourth data sets comprisingdriving-relevant object data, the driving-relevant object data furthercomprising at least location information and detailed object-basedinformation about at least one of a road object, a road furnitureobject, a geographic object or a further object related to driving, themethod comprising the steps of a) a vehicle requesting information aboutan area of interest or an area from the server database, or the serverdatabase triggering a data transfer, b) the server database generatingat least one first data set related to the request or trigger fromfourth data sets stored in the server database, each first data setcomprising driving-relevant object data, c) the server databaseforwarding the at least one first data set to the vehicle which storesthe first data sets in a vehicle database, d) the vehicle collecting atleast one ambient data set along at least a specific section of its pathand/or at a specific time or time interval and/or at least one specificobject by at least one sensor of the vehicle, the ambient data setsrelating to at least one of driving-relevant objects, road furniture andgeographic objects, e) the vehicle performing the first step of objectdata processing by evaluating the at least one ambient data sets andgenerating at least one second data set based on the evaluation result,each second data set comprising driving-relevant object data, f) thevehicle generating third data sets containing differences between theobject-based information of the second data sets and the object-basedinformation of the first data sets related to the section of the road orthe object-based information of the complete second data sets related tothe section of the road, g) the vehicle forwarding the third data setsto the server database, h) the server database storing a selection ofthe third data sets of a plurality of path sections at least partiallyoverlapping each other, i) the server database generating at least onegroup of objects in each path section by grouping selected objectstogether and matching the location information with the locationinformation of groups comprising the same objects of other data setsand/or matching the path sections by aligning the groups of objectsbetween the path sections, j) the server database performing the secondstep of object data processing comprising at least statisticalevaluation and post processing of the third and fourth data sets by theserver database, and k) the server database updating of the fourth datasets in the server database based on the third data sets.
 2. The methodaccording to claim 1, characterized in, that in the first step of objectdata processing the vehicle is generating at least partial object dataof at least parts of driving-relevant objects within a detection rangeof the at least one sensor of the vehicle.
 3. The method according toclaim 1, characterized in, that the second processing step of objectdata processing of the server database comprises assembling object dataof the third and fourth data sets to updated fourth data sets providinga consolidated model of the road network further providingdriving-relevant information.
 4. The method according to claim 1,characterized in, that at least one group comprises a road object and atleast one of a road furniture object and a geographic object.
 5. Themethod according to claim 1, characterized in, that the server databasetransmits at least one of the following to the vehicle: a) a samplingrequest, which preferably specifies the time and/or location of whichinformation should be sampled by the vehicle, b) a special processingrequest of a specific object or area, c) a request for a high resolutionimage preferably of an unknown area, or d) specific processinginstructions or specific program code.
 6. The method according to claim1, characterized in, that the at least one first or third data setscomprise auxiliary data further comprising at least one of a confidencelevel or a statistical parameter like an average value or a standarddeviation parameter related to the object based data in the data set. 7.The method according to claim 1, characterized in, that the serverdatabase is hosted by at least one stationary server or a cloud basedinfrastructure or a combination of both or another embodiment, externalto the vehicle.
 8. The method according to claim 1, characterized in,that the vehicle selects the at least one specific section of the pathbased on the auxiliary data to cover sections of the path having datawith at least one of a low confidence level, a large standard deviationor no data.
 9. The method according to claim 1, characterized in, thatthe vehicle collects at least one ambient data set by using at least oneof an image sensor like a CCD sensor, an infrared sensor, a laserscanner, an ultrasound transducer, a radar sensor, an RF channel sensoror other environmental sensor.
 10. The method according to claim 1,characterized in, that before the step of updating of the fourth datasets, the server database correlates the location information of thestored data with satellite image information and/or further precisionposition information available for at least some of the road objectsspecified by the driving-relevant object data.
 11. A server databasestoring fourth data sets comprising driving-relevant object data, thedriving-relevant object data further comprising at least locationinformation and detailed object-based information about at least one ofa road object, a road furniture object, a geographic object, or afurther object related to driving the server database having means forgenerating first data sets from the fourth data sets, the first datasets being subsets of the fourth data sets, further corresponding tospecific section of a vehicle's path and/or at a specific time or timeinterval and/or at least one specific object and having further meansfor forwarding the first data sets to the vehicle, each first data setcomprising driving-relevant object data, the server database havingfurther means for receiving and storing of third data sets of aplurality of path sections at least partially overlapping each otherfrom a plurality of vehicles, the third data sets containing differencesbetween second data sets having object-based information collected byrespective vehicles and the object-based information of the first datasets, the server database generating at least one group of objects ineach path section by grouping selected objects together and matching thelocation information with the location information of groups comprisingthe same objects of other data sets and/or matching the path sections byaligning the groups of objects between the path sections, the serverdatabase having means for statistical evaluation and post processing ofthe third and fourth data sets and for updating the fourth data sets byusing the results thereof.
 12. A vehicle having means for transferringinformation about an area of interest and/or a path to a remote serverdatabase and for receiving first data sets comprising driving-relevantobject data, such driving-relevant object data further comprising atleast location information and detailed object-based information aboutat least one of a road object, a road furniture object and a geographicobject, within an area of interest and/or a path and/or at a specifictime and/or a specific object, the vehicle having at least one sensorfor collecting at least one ambient data set along at least a specificsection of its path and/or at a specific time and/or a specific object,the ambient data sets relating to at least one of road objects, roadfurniture and geographic objects, the vehicle further having means forevaluating the at least one ambient data sets and generating at leastone second data set based on the evaluation result, each second data setcomprising driving-relevant object data and means for generating thirddata sets containing differences between the object-based information ofthe second data sets and the object-based information of the first datasets, and the vehicle having means for forwarding the third data sets tothe server database.